Key result
PM2.5, smoking, and non-adherence are linked to ~67% of county-level variance in CVD mortality.
Why the study?
Cardiovascular disease remains a primary cause of mortality with persistent geographic disparities, requiring continual updates of localized variations to develop targeted interventions.
Observational
Geospatial and machine learning analyses highlight that COPD, medication non-adherence, smoking, and PM2.5 exposure are dominant predictors of CVD mortality disparities across U.S. counties.
Supports prioritizing PM2.5, smoking, and adherence efforts; extends county disparity mapping but remains hypothesis-generating.
INTRODUCTION: Cardiovascular disease (CVD) remains a primary cause of mortality worldwide, with persistent geographic disparities driven by a complex interplay of risk factors. Continual updates of localized variations in CVD mortality are essential to develop targeted interventions for optimizing disease and healthcare management. METHODS: This study investigated associations between CVD mortality and a comprehensive set of biological, environmental, behavioral, and socioeconomic factors across all U.S. counties, employing correlation, geospatial visualization, stepwise multiple regression, and machine learning models to evaluate the importance of risk associations. RESULTS: Significant disparities in CVD mortality trend were observed across race, age, sex, and region, with elevated rates among older adults, men, and Blacks, particularly in southeastern states exhibiting severe social vulnerability. Correlation analysis identified disease management (e.g., COPD, hypertension, medication non-adherence), environmental factors (PM2.5), lifestyle behaviors (e.g., smoking, sleep duration), and socioeconomic status (e.g., poverty, single-parent households, education) as important contributors to CVD mortality. Conversely, higher household income, physical activity, and cardiac rehabilitation participation were strong protectors. Multiple regression explained 66.9% variance in CVD mortality, recognizing PM2.5, smoking, and medication non-adherence as top associated factors. Random Forest models underscored COPD's predictive dominance, followed by medication non-adherence, smoking, and sleep duration. CONCLUSIONS: The findings highlight the geospatial connection of risk factors to CVD mortality disparities across U.S. counties. They emphasize the critical importance of data-driven strategies targeting air quality, tobacco control, social inequities, and chronic disease management to mitigate CVD burden and promote health equity.
No takes yet. Share an insight, caveat, or question.
David H. An (2025) conducted an observational in Cardiovascular disease mortality. Biological, environmental, behavioral, and socioeconomic risk factors was evaluated on Cardiovascular disease mortality rate (deaths per 100,000 population). Multiple regression explained 66.9% of the variance in county-level CVD mortality, identifying PM2.5, smoking, and medication non-adherence as top associated factors.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: